H3 Grid Support"

In hexify: Equal-Area Hex Grids on the 'Snyder' 'ISEA' 'Icosahedron'

CAN_RUN <- requireNamespace("sf", quietly = TRUE)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  eval = CAN_RUN
)
library(hexify)

hexify v0.5.0 adds H3 as a first-class grid type alongside ISEA. Every core function --- hexify(), grid_rect(), grid_clip(), get_parent(), get_children() --- works with both grid systems through the same interface. This vignette covers what H3 is, how to use it in hexify, and when to prefer it over ISEA.

What is H3?

H3 is a hierarchical hexagonal grid system developed by Uber. It partitions Earth's surface into hexagonal cells at 16 resolutions (0--15), each roughly 7$\times$ finer than the last. Cell IDs are 64-bit integers encoded as hexadecimal strings (e.g., "8528342bfffffff").

H3 has become an industry standard adopted by the FCC, Foursquare, and numerous geospatial platforms.

Key difference from ISEA: H3 cells are not equal-area. Cell area varies by ~1.6$\times$ between the largest and smallest hexagons at any given resolution, depending on latitude. For rigorous equal-area analysis, use ISEA. For interoperability with H3 ecosystems, use type = "h3".

Getting Started

Create an H3 grid by passing type = "h3" to hex_grid():

library(sf)
library(ggplot2)

# Create an H3 grid specification
grid_h3 <- hex_grid(resolution = 5, type = "h3")
grid_h3

Then use hexify() with the grid object, just like ISEA:

# Sample cities
cities <- data.frame(
  name = c("Vienna", "Paris", "Madrid", "Berlin", "Rome",
           "London", "Prague", "Warsaw", "Budapest", "Amsterdam"),
  lon = c(16.37, 2.35, -3.70, 13.40, 12.50,
          -0.12, 14.42, 21.01, 19.04, 4.90),
  lat = c(48.21, 48.86, 40.42, 52.52, 41.90,
          51.51, 50.08, 52.23, 47.50, 52.37)
)

result <- hexify(cities, lon = "lon", lat = "lat", grid = grid_h3)
result

H3 cell IDs are character strings, unlike ISEA's numeric IDs:

# Cell IDs are hexadecimal strings
result@cell_id

# All standard accessors work
cells(result)
n_cells(result)

Choosing Resolution by Area

If you think in terms of cell area rather than resolution numbers, pass area_km2 instead of resolution. hexify picks the closest H3 resolution:

grid_area <- hex_grid(area_km2 = 500, type = "h3")
grid_area

Grid Generation

All grid generation functions work with H3 grids.

Rectangular Region

# Generate H3 hexagons over Western Europe
grid_h3 <- hex_grid(resolution = 3, type = "h3")
europe_h3 <- grid_rect(c(-10, 35, 25, 60), grid_h3)

# Basemap
europe <- hexify_world[hexify_world$continent == "Europe", ]

ggplot() +
  geom_sf(data = europe, fill = "gray95", color = "gray60") +
  geom_sf(data = europe_h3, fill = NA, color = "#E6550D", linewidth = 0.4) +
  coord_sf(xlim = c(-10, 25), ylim = c(35, 60)) +
  labs(title = sprintf("H3 Resolution %d Grid (~%.0f km² avg cells)",
                       grid_h3@resolution, grid_h3@area_km2)) +
  theme_minimal()

Clipping to a Boundary

# Clip H3 grid to France
france <- hexify_world[hexify_world$name == "France", ]
grid_h3 <- hex_grid(resolution = 4, type = "h3")
france_h3 <- grid_clip(france, grid_h3)

ggplot() +
  geom_sf(data = france, fill = "gray95", color = "gray40", linewidth = 0.5) +
  geom_sf(data = france_h3, fill = alpha("#E6550D", 0.3),
          color = "#E6550D", linewidth = 0.3) +
  coord_sf(xlim = c(-5, 10), ylim = c(41, 52)) +
  labs(title = sprintf("H3 Grid Clipped to France (res %d)", grid_h3@resolution)) +
  theme_minimal()

Hierarchical Navigation

H3's killer feature is its clean hierarchical structure: every cell has exactly one parent and seven children. hexify exposes this with get_parent() and get_children().

Parents

# Get parent cells (one resolution coarser)
grid_h3 <- hex_grid(resolution = 5, type = "h3")
child_ids <- lonlat_to_cell(
  lon = c(16.37, 2.35, 13.40),
  lat = c(48.21, 48.86, 52.52),
  grid = grid_h3
)

parent_ids <- get_parent(child_ids, grid_h3, levels = 1)
data.frame(child = child_ids, parent = parent_ids)

Children

# Get children of a single cell (one resolution finer)
grid_coarse <- hex_grid(resolution = 3, type = "h3")
coarse_id <- lonlat_to_cell(16.37, 48.21, grid_coarse)

children <- get_children(coarse_id, grid_coarse, levels = 1)
cat(length(children[[1]]), "children at resolution", grid_coarse@resolution + 1, "\n")
head(children[[1]])

Visualizing the Hierarchy

# Parent cell polygon
parent_poly <- cell_to_sf(coarse_id, grid_coarse)

# Children cell polygons
grid_fine <- hex_grid(resolution = 4, type = "h3")
children_poly <- cell_to_sf(children[[1]], grid_fine)

ggplot() +
  geom_sf(data = children_poly, fill = alpha("#E6550D", 0.3),
          color = "#E6550D", linewidth = 0.5) +
  geom_sf(data = parent_poly, fill = NA, color = "black", linewidth = 1.2) +
  labs(title = sprintf("H3 Hierarchy: 1 parent (res %d) → %d children (res %d)",
                       grid_coarse@resolution,
                       length(children[[1]]),
                       grid_fine@resolution)) +
  theme_minimal()

Working with H3 Data

The standard hexify workflow applies to H3 grids. Here's a complete example using simulated species observations:

set.seed(42)

# Simulate observations across Europe
obs <- data.frame(
  lon = c(rnorm(200, 10, 12), rnorm(100, 25, 8)),
  lat = c(rnorm(200, 48, 6), rnorm(100, 55, 4)),
  species = sample(c("Sp. A", "Sp. B", "Sp. C"), 300, replace = TRUE)
)
obs$lon <- pmax(-10, pmin(40, obs$lon))
obs$lat <- pmax(35, pmin(65, obs$lat))

# Hexify with H3
grid_h3 <- hex_grid(resolution = 3, type = "h3")
obs_hex <- hexify(obs, lon = "lon", lat = "lat", grid = grid_h3)

# Aggregate: species richness per cell
obs_df <- as.data.frame(obs_hex)
obs_df$cell_id <- obs_hex@cell_id

richness <- aggregate(species ~ cell_id, data = obs_df,
                      FUN = function(x) length(unique(x)))
names(richness)[2] <- "n_species"

# Map it
polys <- cell_to_sf(richness$cell_id, grid_h3)
polys <- merge(polys, richness, by = "cell_id")

europe <- hexify_world[hexify_world$continent == "Europe", ]

ggplot() +
  geom_sf(data = europe, fill = "gray95", color = "gray70", linewidth = 0.2) +
  geom_sf(data = polys, aes(fill = n_species), color = "white", linewidth = 0.3) +
  scale_fill_viridis_c(option = "plasma", name = "Species\nRichness") +
  coord_sf(xlim = c(-10, 40), ylim = c(35, 65)) +
  labs(title = "Species Richness on H3 Grid",
       subtitle = sprintf("H3 resolution %d (~%.0f km² avg cells)",
                          grid_h3@resolution, grid_h3@area_km2)) +
  theme_minimal() +
  theme(axis.text = element_blank(), axis.ticks = element_blank())

ISEA--H3 Crosswalk

hexify v0.6.0 added h3_crosswalk() for bidirectional mapping between ISEA and H3 cell IDs. This is useful when you work in ISEA for analysis but need to share results with H3 ecosystems (or vice versa).

# Start with an ISEA grid and some cells
grid_isea <- hex_grid(resolution = 9, aperture = 3)
isea_ids <- lonlat_to_cell(
  lon = c(16.37, 2.35, 13.40, -3.70, 12.50),
  lat = c(48.21, 48.86, 52.52, 40.42, 41.90),
  grid = grid_isea
)

# Map ISEA cells to their closest H3 equivalents
xw <- h3_crosswalk(isea_ids, grid_isea)
xw[, c("isea_cell_id", "h3_cell_id", "isea_area_km2", "h3_area_km2")]

The area_ratio column shows how ISEA and H3 cell sizes compare --- values close to 1 mean the resolutions are well-matched.

ISEA vs H3: When to Use Which

| | ISEA | H3 | |---|---|---| | Cell area | Exactly equal | ~1.6$\times$ variation | | Cell IDs | Numeric (integer) | Character (hex string) | | Apertures | 3, 4, 7, 4/3 | Fixed (7) | | Resolutions | 0--30 | 0--15 | | Hierarchy | Approximate (aperture-dependent) | Exact (7 children per parent) | | Dependencies | None (built-in C++) | None (vendored H3 C library) | | Industry adoption | Scientific / government | Tech industry / commercial |

Use ISEA when:

• Equal-area cells are required (biodiversity surveys, density estimation, statistical sampling)

• You need fine control over aperture and resolution

• No external dependencies are acceptable

Use H3 when:

• Interoperability with H3 ecosystems (Uber, Foursquare, DuckDB, BigQuery)

• Clean hierarchical operations (parent/child traversal) are a priority

• Slight area variation across latitudes is acceptable for your analysis

Resolution Reference

h3_res <- hexify_compare_resolutions(type = "h3", res_range = 0:15)
h3_res$n_cells_fmt <- ifelse(
  h3_res$n_cells > 1e9,
  sprintf("%.1fB", h3_res$n_cells / 1e9),
  ifelse(h3_res$n_cells > 1e6,
         sprintf("%.1fM", h3_res$n_cells / 1e6),
         ifelse(h3_res$n_cells > 1e3,
                sprintf("%.1fK", h3_res$n_cells / 1e3),
                as.character(h3_res$n_cells)))
)
knitr::kable(
  h3_res[, c("resolution", "n_cells_fmt", "cell_area_km2", "cell_spacing_km")],
  col.names = c("Resolution", "# Cells", "Avg Area (km²)", "Spacing (km)"),
  digits = 1
)

Areas are averages --- actual cell area varies by latitude.

See Also

• vignette("quickstart") - Getting started with hexify (ISEA-focused)

• vignette("workflows") - Grid generation, multi-resolution analysis, GIS export

• vignette("visualization") - Plotting with plot(), hexify_heatmap()

• vignette("theory") - Mathematical foundations of the ISEA projection

hexify documentation built on March 1, 2026, 1:07 a.m.